An artificial cognitive system may be defined as a data processing system, supplied with sensor signals, e.g., video signals, performing a number of tasks and capable of reproducing one or several aspects of human performance with respect to those tasks.
Some aspects of human performance are especially relevant in the context of an adaptive driver assistance system. First, the ability to learn from previous experiences and to use what has been learned to successfully handle unknown but qualitatively similar tasks or situations. Second, robustness against incomplete, inconsistent or distorted input data when performing a task in a real-world scenario, instead of an artificial one. Third, the ability to produce results even in critical situations, where said situations can arise due to strong variations in the system's input data (which usually come from observing the external world), or due to impaired performance of one or several internal processing steps.
In any case, a breakdown of the driver assistance system must be avoided. Finally, of particular relevance is the universal possibility to incrementally extend the driver assistance system by new functions, partly or wholly specified by learning procedures, which can access and use all data that is contained within the driver assistance system without additional design efforts.
A promising approach for implementing the above features in an adaptive driver assistance system is to use a neural network. The basic concepts of neural field dynamics in the discrete and continuous case, as well as of learning mechanisms operating on neural fields, have been formulated in Amari, S. “Dynamics of pattern formation in lateral inhibition type neural fields”, Biological Cybernetics 27:77-87, and Amari, S. “Mathematical foundations of Neurocomputing”, Proceedings of the IEEE 78:1443-1463; Taylor, J. G. “Neural ‘bubble’ dynamics in two dimensions: foundations”, Biological Cybernetics 80:393-409 which are incorporated by reference herein in their entirety.
More specifically, the modelling approach of Amari dynamics includes the concepts of cooperation and competition, as well as attractor dynamics. The concept of neural cooperation and competition implies a similarity measure on stored data, which is used to iteratively suppress similar and enhance dissimilar information. This leads to a final stable network state where the stored information is largely free of redundancies. Such stable states are furthermore characterized by a considerable robustness against noise or inconsistent inputs, since the cooperation and competition principles in the network push the system back to stability in such cases. This is due to the fact that the neural networks as proposed by Amari implement attractor dynamics where it can be shown that stable states always have this robustness property.
However, the methods described by Amari et al. only model single neural sheets. Furthermore, these references do not address the way data is encoded.
The advantages and cognitive properties of representations based on Amari dynamics or similar dynamics have been discussed in, for example, Cisek, P. “Integrated neural processes for defining potential actions and deciding between them: a computational model. Journal of Neuroscience 26:9761-9770; Engels, C. et al. “Dynamic fields endow behavior based robots with representations”, Robotics and Autonomous Systems 14:55-77; and Erlhagen et al. “Dynamic field theory of movement preparation”, Psychological Review 109:545-572 which are incorporated by reference in their entirety. However, these publications describe comparatively small systems used for conceptual demonstration only. The issue of learning is not addressed in these references, neither is any mechanism for it.
Simple learning mechanisms are discussed in Konen et al. “A fast dynamic link marching algorithm for invariant pattern recognition”, Neural Networks 7:1019-1030 which is incorporated by reference herein in its entirety. The learning mechanism employs the dynamic properties of dynamic representations as well as a maximum-detection scheme for identifying which weight should be adapted. However, the above learning mechanism does not employ neural maps, the weights between two sheets have an all-to-all connectivity from the beginning and there is no approximation scheme that exploits the intrinsic blob-forming properties of Amari dynamics. In addition, the article by Konen et al. primarily deals with unstructured inputs from visual sensors.
In addition, German patent DE 19 844 364 (M. Giese) which is incorporated by reference herein in its entirety discloses a system of neural sheets performing transformations between various coordinate systems, where data in sheets is represented by population codes, i.e., a common data encoding scheme (CDES), where the sheets possess time dynamics similar to Amari dynamics and information is transmitted between multiple sheets. However, DE 198 44 364 does not discuss a learning system (LS).